{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:01:25Z","timestamp":1772164885495,"version":"3.50.1"},"reference-count":41,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":254,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant\u2019s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.<\/jats:p>","DOI":"10.1162\/imag_a_00301","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T16:56:04Z","timestamp":1726073764000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings"],"prefix":"10.1162","volume":"2","author":[{"given":"Victoria","family":"Peterson","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States"},{"name":"Instituto de Matem\u00e1tica Aplicada del Litoral, IMAL, UNL, CONICET, Santa Fe, Argentina"},{"name":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Vissani","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyu","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinwan","family":"Rabbani","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan E.","family":"Crone","sequence":"additional","affiliation":[{"name":"Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Bush","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R. 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